Plot gap identification

ABSTRACT

Field data is collected of a field. Each instance of field data contains information that can be used to determine a value corresponding to whether or not a plant is present or absent in a particular location and is referred to as a plant presence value. The plant presence values are aggregated using the position data associated with each instance of field data to generate aggregated plant presence values. Gaps between plots are identified based partly on variations in the plant presence values within the aggregated field data. Information known about a field can be used to heuristically identify gaps in a seed line or used to eliminate locations on a seed line that may look like a gap based on low plant presence values. The aggregated plant presence values can be presented as a heat map of plant presence values showing the relative plant density of the field.

BACKGROUND

Crop growers plant different seed variants by genotype in individualplots of a field and often record the location of each different seedvariant by plot to track, measure, and be able to compare thepotentially different growth performances among the different seedvariants. In order to measure and compare physiological parameters thatdetermine growth performance, information of the plants or crops atvarious stages of growth must be collected and accurately mapped totheir actual location in the grower's field. Mapping this collectedinformation to a field, however, is not a straightforward process. Forexample, a grower may record the location of a plot with a positioningsystem that records slightly different measurements relative to anotherpositioning system used to collect the information, the plot lengths maynot be the same length (relative to a recorded theoretical plot lengthprovided by the grower) across all plots in the field, among othervariations and anomalies. Further, collection of the information couldfail for a portion of the field (e.g., a seed line was inadvertentlyskipped, etc.) that could inadvertently and incorrectly map informationof the plants to incorrect plots, thereby, rending all subsequentlycollected field data incorrect from that point. Thus, any error howeversmall can result in significant measurement and comparison error in theaggregate across a field.

SUMMARY

Since a grower's position data and/or plot length data can be oftenunreliable or inconsistent relative to information collected of a field,a field data collection and analysis system delineates the field byidentifying individual plots using the collected field data. Field datacollection system navigates through or over a field collecting the fielddata, such as images of plants or crops growing in the field. In orderto track and measure the growth of different seed variants, growersplant seeds by genotype and record the location of that genotype in aplot. Thus, the field data can be mapped to the grower's plantingrecords in order to provide the grower with information for the correctgenotype and its respective location in the field. As part of thecollected information, there will generally be at least some gapsbetween different genotypes, but gaps may also occur as a result ofdifferent growth and germination rates (phenotypes) among the differentgenotypes. The gaps between plots provide a visual indicator linking theraw field data to an actual plot in the field. For example, if a groweris interested in more information on a first genotype located in aparticular plot of a particular seed line (e.g., the 5^(th) plot on thesecond seed line), the field data can be quickly accessed when segmentedinto plots as opposed to scrolling through a mass of raw data.

Gaps, in one example, can be defined or characterized by an absence ofplants along a seed line (because the grower does not plant in thegaps). By this definition, gaps may inadvertently appear as a result ofdifferent growth and germination rates among the different genotypes orother issues with a particular location in the field and so forth. Thus,in order to properly segment and map the field data to an actuallocation in the field, it can be important to positively identify gapsbetween plots from potentially false gaps (e.g., areas that look likegaps) and, thus, not confuse or misidentify an area of poor growth inthe middle of a plot with a gap between plots, for example.

Accordingly, each instance of field data contains information that canbe used to determine a value corresponding to whether or not a plant ispresent or absent in a particular location and is referred to as a plantpresence value. The plant presence values are aggregated using theposition data associated with each instance of field data to generateaggregated plant presence values and gaps between plots are identifiedbased partly on variations in the plant presence values within theaggregated field data. Information known about a field can be used toheuristically identify gaps in a seed line or used to eliminatelocations on a seed line that may look like a gap based on low plantpresence values. The aggregated plant presence values can be presentedas a heat map of plant presence values showing the relative plantdensity of the field.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a flow diagram for identifying plots of a field using fielddata captured of a field, in one embodiment.

FIG. 2 is a block diagram of a system environment in which gapidentification can be performed, in one embodiment.

FIG. 3 is an example portion of a field that includes multiple seedlines and multiple plots within each seed line, in one embodiment.

FIGS. 4A-4C show an example sequence of images captured along a seedline, in one embodiment.

FIG. 5 shows an example plant presence heat map for individual plants ofa field, in one embodiment.

FIG. 6 shows an example of determining gaps from plant presence values,in one embodiment.

FIG. 7 shows an example of plot location refinement, in one embodiment.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles described herein.

DETAILED DESCRIPTION

Since a grower's position data and/or plot length data can be oftenunreliable or inconsistent relative to information collected of a field,a field data collection and analysis system identifies individual plotsin a field. FIG. 1 shows flow diagram 100 for identifying plots of afield using field data collected by a field data collection system, inan embodiment. The field data collection system collects field data 102of plants or crops in a field using one or more sensors. Each instanceof field data 102 contains information that can be used to determine avalue corresponding to whether or not a plant is present or absent in aparticular location. This value, in various embodiments, is referred toas plant presence value 104 for generally providing a relativeindication of the presence or absence of plant matter captured by thefield data.

Accordingly, each instance of field data 102 is analyzed to generateplant presence data 104 for the field. Then plant presence values 104are aggregated using position data associated with each instance offield data 102 to generate aggregated plant presence values 106 and,based partly on variations in the plant presence values within theaggregated field data, gaps between plots are identified. The aggregatedplant presence values 106 can be presented as a heat map of plantpresence values showing the relative plant density of the field.Accordingly, gap identification 108 is performed to identify candidategaps between plots by identifying areas of the field associated with lowto zero plant presence (e.g., below a plant presence value threshold) inthe aggregated plant presence data.

Field characteristic data 110 is information that is known about thefield that can be used heuristically to aid in identifying gaps in aseed line, which may also be referred to as a row. Field characteristicdata 110 includes data, such as GPS/location/position data for plot andfield boundaries, plot length, expected gap length (as planted), seedline length (as planted), direction of seed lines, total number of seedlines and/or plots, stage of growth of plants in a respective plot(since some plots may have been planted before others), type of plant ineach plot (since different plants have different growth rates and sizes,and to distinguish desired plants from weeds), how many plots areplanted at a particular time (similar to stage of growth), and otherfield parameters and plant characteristics. Field characteristic data110 can, thus, be used to eliminate locations on a seed line that maylook like a gap when analyzing the plant presence values 104 inisolation.

Accordingly, plot refinement 112 uses field characteristic data 110 torefine the beginning and end of each plot, for example, based on thecandidate gaps determined using the aggregated plant presence values106. For example, the fact that gaps are located at predeterminedlengths (e.g., 20 ft) can be used to refine the location that marks thebeginning and end of a respective plot. This may help prevent theoccasional location where plants are missing on one or more seed lines.

The result of the process illustrated in flow diagram 100 is theidentification of real gaps between plots in a field determined byanalyzing the field data. The process of flow diagram 100 can becompletely automated and performed in real-time or portions can beachieved with user assistance.

Field Data

Field data 102 is collected, captured, or otherwise obtained from afield by a collection system that includes at least one sensor. FIG. 2is a block diagram of system environment 200 that includes field datacollection system 202, in one embodiment. Field data collection system202 includes data collection module 204, positioning system 206,collection store 208, and sensor 210. In one embodiment, field datacollection system 202 is navigated through or over a field whilecollecting field data 102 using sensor 210.

Positioning system 206 is a navigation system, such as GlobalPositioning System (GPS), that provides location and/or time informationof field data collection system 202 as each instance of field data 102is collected. The location information is associated with or mapped toan instance of field data 102 corresponding to the location of fielddata collection system 202 within the field when the instance of fielddata 102 is collected. Other positioning systems, such as systems usingtriangulation to determine the location of field data collection system202 within the field, can alternatively be used.

Data collection module 204 contains logic for collecting field data 102and additionally receives position data from positioning system 206,associates the position data with each instance of field data 102, andstores field data 102 with the corresponding position data in collectionstore 208 as field data collection system 202 navigates through thefield.

Collection store 208 receives field data 102 with the correspondingpositioning data from data collection module 204, stores the field data,and makes the field data available to field data analysis system 230,which will be described further below.

Sensor 210 can be one or more digital cameras, near infrared (IR)cameras, a thermal camera, a plant height sensor (e.g., mechanicalsensor or combination image capture and software to identify changes inrelative plant heights), a plant stalk detection sensor, and so forth.Imaging data captured by sensor 210 can be multi-spectral orhyper-spectral.

As summarized above, field data 102 is any type of data capable of beingused to identify or differentiate individual plants in a seed linecaptured, collected, or otherwise obtained by sensor 210. For example,field data 102 can be thermal image data, color image data, plant heightdata, plant volume, and so forth and field data 102 can bemulti-spectral or hyper-spectral, a single image or multiple imagesstitched together captured from above (e.g., from a plane or satellite),among others. Alternatively, field data 102 is any data capable of moregenerally being used to generate a binary output corresponding to thepresence or absence of plants in a location. An individual instance offield data 102 can be granular, representing an individual plant in alocation, or more general, representing an area along a seed line thatmay include two or more plants.

In a field, growers insert gaps between different genotypes of plantsand it is common to plant an individual genotype in a single plotseparated from adjacent plots by a gap. FIG. 3 is an example portion offield 300 though which field data collection system 202 is navigated tocollect field data 102, in one embodiment. Field 300 includes multipleseed lines (302 a-302 e, collectively “302”), multiple plots (e.g., 306,308, and 310) within each seed line 302, gaps (304 a-304 j, collectively“304”), between plots (306, 308, and 310), and false gaps (312 a-312 c,collectively “312”) located in the middle of a few plots (e.g., 310).

Accordingly, field data collection system 202 is navigated through orover field 300 while collecting field data 102 along seed lines 302 viasensor 210. Navigation and drive of field data collection system 202 canbe autonomous, performed by a vehicle, such as a tractor, truck,unmanned aerial vehicle (UAV), and so forth. In one example, field datacollection system 202 is shrouded to isolate the data collection processto plants along a single seed line and, therefore, eliminate plants inadjacent seed lines from appearing in the data. Additionally, the shroudmay provide illumination enabling data collection to be performed atnight if necessary.

As described above, many test plots are planted each year to collectdata for the growth quality and uniformity of different seed variants atvarious different stages of growth. To test different seed variants, thegrowers typically grow the same genotype in a single plot. For example,if a grower were testing the phenotypes of three different genotypes ofcorn, the grower might plant the first genotypes in plot 306, the secondgenotypes in plot 308, and the third genotypes in plot 310. In order toisolate each phenotype, the plots (306, 308, and 310) are separated bygaps (304 a, 304 b) where no plants are planted to isolate eachindividual phenotype. In this example, plot 306 is separated by plot 308by gap 304 a and plot 308 is separated by plot 310 by gap 304 b and thefield data can be used to identify gaps (304 a-304 j) based onvariations in the field data and prior known information about field300.

Gaps, in one example, can be defined or characterized by an absence ofplants along a seed line (because the grower does not plant in thegaps). By this definition, gaps may inadvertently appear as a result ofdifferent growth and germination rates among the different genotypes orother issues associated with a particular location in a field and soforth. For example, FIG. 3 additionally shows false gaps (312 a, 312 b,and 312 c, collectively “312”) in the middle of a plot. False gaps 312could be the result of soil anomalies, bad seeds, poor soil,insufficient light, areas where seed were inadvertently not sown, and soforth. In order to properly segment and map field data 102 to an actuallocation in field 300, it is thus important to positively identify realgaps between plots from false gaps 312 and, thus, not confuse ormisidentify an area of poor growth in the middle of a plot with a gapbetween plots, for example.

Misidentifying an area of poor growth in the middle of a plot as a gapcan result in an incorrect mapping of the collected field data 102 toplants in field 300. For example, if an instance of the collected fielddata reflected an area of robust plant growth corresponding to a firstseed variant, but was incorrectly mapped to an area of poor or averageplant growth corresponding to a second seed variant, the grower wouldmistakenly conclude the second seed variant as the superior seed variantover the actually superior first seed variant. Thus, an accurate mappingbetween field data 102 and field 300 is important and an accuratemapping begins with properly identifying gaps 304.

Other than weeds, there are no plants growing (or at least intentionallyplanted) in gaps 304. Therefore, gaps 304 should have zero to low plantgrowth relative to the plots each gap separates. With this insight, ametric for the relative desired plant density observable or determinablefrom field data 102 is defined as plant presence value 104 and the plantpresence values of field 300 are determined from the collected fielddata 102, as described below.

Plant Presence Values

Accordingly, a plant presence value 104 is determined from each instanceof field data 102. As briefly described above, each instance of fielddata 102 contains information that can be used to determine a numericalvalue corresponding to whether or not a plant is present or absent in alocation. This value, in various embodiments, is referred to herein asthe “plant presence” value 104 for generally providing a relativeindication of the presence or absence of plant matter across field 300(e.g., degree of plant presence, amount of plant matter, etc.) capturedwithin field data 102. Thus, each instance of field data 102 isprocessed or analyzed by field data analysis system 230 to generateplant presence data 104 for the field.

Referring to FIG. 2, system environment 200 further includes field dataanalysis system 230, in one embodiment, and field data analysis system230 includes data processing module 232. Data processing module 232receives the collected field data 102 from collection store 208 of fielddata collection system and determines the plant presence value 104 foreach instance of field data 102. The plant presence values can be binary(i.e., there is or is not a plant in this location) or represent a valuewithin a range of values, such as plant height, plant volume, leafsurface area, the number of green pixels in the field data, a ratio ofgreen pixels to brown pixels, and so forth, that represents some measureof an amount of plant growth.

FIGS. 4A-4C show example instances of field data 102 collected along aseed line 302, in one embodiment. In this example, sensor 210 is acamera and each of the instances of field data 102 is an image (400 a,400 b, 400 c). Accordingly, data processing module 232 analyzes eachimage (400 a, 400 b, 400 c) to determine the plant presence value 104for each image (400 a, 400 b, 400 c). In one embodiment, analyzing eachinstance of field data 102 includes analyzing pixels of images (400 a,400 b, 400 c) to determine the plant presence value 104 for each ofthese instances of field data. In one example, the plant presence value104 for each of these instances of field data 102 is based on the numberof green pixels in each of images (400 a, 400 b, 400 c). Additionally,the number of pixels can be normalized (e.g., based on the average,median, max, or other measurement taken across the field) for aparticular field (to enable the relative comparison among the differentplots).

In one embodiment, each image (400 a, 400 b, 400 c) is cropped or onlypixels in central portion (404 a, 404 b, 404 c) of each image (400 a,400 b, 400 c) are analyzed in an attempt to isolate individual plants402 in each image. In this example, the plant presence value can be ameasure of “greenness”, that is, a percentage of green pixels in centralportion (404 a, 404 b, and 404 c), a ratio of green pixels to brownpixels, or any other calculation capable of comparing the number ofgreen pixels in an image relative to the number of green pixels in otherimages of the field. Accordingly, FIGS. 4A-4C each correspond to adifferent plant presence value. In this example, a first plant presencevalue is calculated from central portion 404 a of image 400 a, a secondplant presence value is calculated from central portion 404 b of image400 b, and a third value is calculated from central portion 404 c ofimage 400 c. In this example, image 400 c is shown illustrated with themost/tallest plants illustrated therein relative to image 400 a andimage 400 b and, therefore, has the highest plant density and thegreatest number of green pixels relative to images 400 a and 400 b.Thus, the third plant presence value corresponding to image 400 c is thehighest among images (400 a, 400 b, 400 c). Image 400 b shows the lowestplant density (almost zero except for a weed or two) and will,therefore, have the lowest plant presence value among images (400 a, 400b, 400 c). The second plant presence value is so low, in this example,that it is indicative of a gap (gap 410).

In other implementations, other methods of determining the plantpresence value 104 for each instance of field data 102 may be used. Forexample, other sensor types 210 as introduced above may produce adifferent kind of information, which may be processed to identifyindividual plants. For example, LIDAR or image data may be used togenerate virtual models of individual plants, in order to identify thepresence of unique features of individual plants, such as stalks orbases. These identified stalks or bases may be used to determine whichportions of sensor data associated with an instance of field data 102are associated with a given plant, and therefore associated with thecorresponding plant presence value 104. Techniques such as this can behelpful in disambiguating which plant matter is associated with whichplant, and therefore, which plant matter is associated with each portionof a plot. More information for using techniques such as this toidentify plant unique features can be found in U.S. ProvisionalApplication No. 62/163,147, which is incorporated by reference herein inits entirety. Another alternate implementation for identifying uniqueplant features using field data 102 captured on per plant basis usingimage sensors 210 is described U.S. Provisional Application No.62/279,599.

Yet further methods may be used to identify plant presence value 104. Asabove, plant height, plant volume, and leaf surface area are allexamples of quantities from the field data 102 that may be used todetermine the plant presence values. These types of field data 102 maycorrespond with different types of sensors, which would in turn beassociated with different processing routines for determining plantpresence values 104 from the raw field data 102. For example, higherplant heights may be associated either with binary plant presence ortaller heights may be associated with higher (analog) plant presencevalues. Plant heights may be gathered using a height sensor, such as atime of flight sensor mounted on a UAV. A correlation function may beused to associate raw plant heights to plant presence values. Similarnormalized differential vegetative indices (NDVI) may be used todetermine plant volume or leaf surface area, which may in turn be mappedto plant presence values using an appropriate correlation function.

Often the field will contain weeds alongside the plants of interest. Thedata processing module 232 is further configured to determine plantpresence values that are based only on plants of interest, and which donot factor in the presence of weeds. Consequently, the presence of fielddata 102 indicating weeds may either be eliminated from inclusion in theplant presence values 104 or it may be associated with a separate plantpresence value (referred to as a weed plant presence value for clarity)stored separately in association with the field data 102 and thatlocation in the field.

Various techniques may be used to process field data 102 so as toseparately identify weed presence for tabulation of the correct“desired” plant presence value and/or the weed plant presence value. Inone embodiment, plant modeling techniques such as those identified inU.S. Provisional Applications Nos. 62/163,147 and 62/279,599, asincorporated herein, can be used to model plant structures and identifyindividual plants. The individual plant models can be used to identifyfeatures associated with the plants, and discriminate desired plants vs.weeds using plant-specific properties derived from the virtual plantmodels.

Further, depending upon the type of sensors 210 used to capture thefield data 102, the field data 102 itself may provide information thatallows discrimination between desired plants and weeds. For example,plant colors, plant heights, plant leaf area index, plant volume, andother observable quantities may be identifiably different in the fielddata 102 between desired plants and weeds, and can accordingly be usedto eliminate or de-weight some instances of field data from contributingto desired plant presence values.

Plant presence values 104 are subsequently aggregated for at least aportion of field 300 as described in the next section. The remainder ofthis discussion discusses only plant presence values 104 generally,however in practice this may include all plants, a subset of plants ofinterest (or plant of interest), weeds or other non-desirables, etc.,depending upon the implementation.

Plant Presence Value Aggregation

Aggregating plant presence values 104 allows for relative comparison ofthe plant presence values and for the identification of patterns in theplant presence values 104 that are reminiscent of gaps (areas associatedwith low plant presence values) between plots (areas associated withhigh plant presence values). Plant presence values 104 for field 300 areaggregated according to their location in field 300 using the positiondata associated with each corresponding instance of field data 102 togenerate aggregated plant presence values 106. Referring to FIG. 2,field data analysis system 230 further includes data aggregation module234, in one embodiment, that aggregates plant presence values 106 toidentify sections within seed lines 302 that have relatively low plantpresence values. Areas of field 300 that have relatively low plantpresence values are one characteristic of gaps between plots and areidentified for further analysis.

In one embodiment, the plant presence values are aggregated into ascaled plant presence heat map composed of each individual plantpresence value 104 for field 300. FIG. 5 shows an example plant presenceheat map 500 for seed line 302 a from FIG. 3, in one embodiment. Thus,field data collection system 202 has obtained field data 102 for seedline 302 a, a plant presence value 104 for each instance of field data102 along seed line 302 a has been determined and subsequentlyaggregated using the position data to generate plant presence heat map500. Accordingly, heat map 500 is composed of a set of plant presencevalues 102 that include individual plant presence values (502 a, 502 b,502 c, 502 d, 502 e, 502 f, and 502 g, collectively “502”) eachcorresponding to an individual instance of field data 102 along seedline 302 a.

Plant presence heat map 500 represents plant density along seed line 302a and the different shades of plant presence heat map 500 representdifferent plant presence values. Plant presence values 502 maycorrespond to individual plants, multiple plants, a predefined length(e.g., 2 feet, 1 meter, etc.) along a seed line, and so forth. In thisexample, dark plant presence values correspond to areas of relativelyhigh plant presence or areas of robust plant growth and light valuescorrespond to areas of low plant presence. At least some of the areas oflow plant presence are gaps.

In this example, plant presence value 502 b is the lightest of plantpresence values 502 and is adjacent to two plant presence values withthe same value, which may correspond to a gap. Similarly, 502 g is alsoa relatively low plant presence value with two adjacent low plantpresence values of the same value. Additionally, plant presence value502 d is also relatively low compared to other plant presence values502. At this point in the process, the three areas in field 300corresponding to plant presence values 502 b, 502 d, and 502 g could beactual gaps, an area associated with bad seeds, a soil anomaly, or otherissue effecting plant growth in these locations. In this example, plantpresence value 502 b corresponds to gap 304 a between plot 306 and plot308 and plant presence value 502 g corresponds to gap 304 b between plot308 and plot 310, although this determination has yet to be made at thispoint in the process. Conversely, plant presence value 502 e is equal tothe darkest plant presence values 502 along seed line 302 a and isindicative of an area of robust plant growth.

Gap Identification

Areas associated with low to zero or low plant presence in aggregatedplant presence data 106 (e.g., heat map 500) are used to identify gaps304 between adjacent plots along seed lines 302. Referring to FIG. 2,field data analysis system 230 further includes, in one embodiment, gapidentification module 236 that identifies gaps between plots based onplant presence values that are associated with or empirically correspondto a gap (e.g., below a plant presence value threshold). The plantpresence value threshold can be a value set empirically by a user whichmay vary based on plant/crop type, phenotype/genotype, stage of growth,and so forth. Alternatively, the plant presence value threshold can bebased on the plant presence values of the field itself, such as beingbelow an average plant presence value for the whole field (or a subsetof the field), some percent of the average, and so forth.

FIG. 6 shows an example process 600 for identifying gaps from plantpresence values 104, in one embodiment. In many fields, plots and thegaps between them typically have the same length, are planted inparallel columns (or rows depending on chosen perspective), and beginfrom the same starting line or edge of a field. This feature, common tomany fields, results in gaps 304 of adjacent seed lines 302 lining up(or being aligned) perpendicularly across seed lines 302, as shown inFIG. 6. Since gaps 304 are expected to line up and there are no plantsat least intentionally planted in those gaps, the average plant presencevalue for a row across field 300 (i.e., perpendicular to seed lines 302)where the gaps 304 are aligned is substantially lower relative to thoseof other rows perpendicular to seed lines 302 and also to the averageplant presence value for the field (or subset of the field where thecrop in question has been planted).

FIG. 6 shows rows (602 a-602 f, collectively “602”) as dashed linesperpendicularly traversing field 300 across each seed line 302 and canbe defined as one or more plant presence values, a unit length of field300 (e.g., each row corresponds to a meter of the length of field 300),or some other unit/metric. FIG. 6 also shows dashed lines correspondingto field boundaries 604 (i.e., a beginning 604 a and an end 604 b of theseed lines 302). Accordingly, the average plant presence value for agiven row 602 is the average of one or more plant presence values fromseed line 302 a, seed line 302 b, seed line 302 c, seed line 302 d, andseed line 302 e on that row, in this example. As shown in FIG. 6, row602 a traverses field 300 in the middle of plots of each seed line 302and will, therefore, have a higher than average plant presence valuecompared to the field average plant presence value that includes gaps.Similarly, row 602 b traverses field 300 in the middle of plots of eachseed line 302; however, row 602 b additionally traverses through falsegap 312. Here, false gap 312 lowers the average plant presence value ofrow 602 b relative to row 602 a, for example, but the average plantpresence value of row 602 b is still much higher relative to a row ofgap plant presence values near zero, such as row 602 c. Further, in thisexample, row 602 d and row 602 f traverse field 300 across plots, buteach traverse through a false gap (312 a, 312 b) and row 602 e isaligned with gaps (304 b, 304 d, 304 f, 304 h, and 304 j) of seed lines302 a-302 e.

Accordingly, row 602 a has the highest average plant presence value,rows (602 b, 602 d, 602 f) have average plant presence values that eachinclude a near zero for single a seed line 302 for false gap (312 a, 312b, 312 c), and rows (602 c, 602 e) have average plant presence valuesnear zero. False gaps (312 a, 312 b) when analyzing each seed line 302individually or in isolation, can be mistaken as gaps between plots. Byaveraging the plant presence values by row 602 across field 300, thepotential for mistaking false gaps (312 a, 312 b) as actual gaps isgreatly reduced while the large contrast between rows corresponding toactual gap locations relative to other rows positioned in the middle ofthe plots is made apparent. Thus, in one embodiment, average plantpresence values for each row 602 is compared to a plant presence valuethreshold. The plant presence value threshold can be the average plantpresence value across field 300, can be computed relative to the plantpresence values of neighboring or adjacent seed lines, and the thresholdmay also be arbitrarily chosen to be larger than values generally knownto be associated with gaps. Since rows 602 corresponding to gaplocations are associated with low average plant presence values, thelocations of rows 602 with average plant presence values below the plantpresence value threshold are identified as gap locations between plots.

The plant presence values can be compared to different plant presencevalue thresholds and at different points in gap identification 108. Inone embodiment, thresholds can be first applied to the plant presencevalues to generate a bit map of field 300 of plant presence heat map500. A bit map of plant presence heat map 500 may appear something likerows 302 in FIG. 6. In this example, values greater than a plantpresence value threshold are assigned a value of 1 and values below areassigned a value of 0. The average plant presence values may also bedetermined first and later compared to the plant presence valuethreshold.

In an alternative embodiment, candidate gaps are determined based onplant presence values below a plant presence threshold. For example,candidate gaps can be determined by identifying multiple consecutiveinstances of field data below the plant presence threshold along a row.Here, all real gaps and false gaps (312 a, 312 b) are initially returnedand identified as candidate gaps. Then, as discussed in more detail withrespect to field characteristic data 110 below, information known aboutthe field is used to eliminate or rule-out false gaps (312 a, 312 b) andconfirm real gaps. For example, if all plots and gaps in field 300 areplanted with approximately predetermined length, such as 20 feet inlength, with predetermined real gaps between them, such as 2 feet inlength, candidate gaps occurring between multiples of the predeterminedlength (length+real gap length), or candidate gaps that are ofinsufficient size (e.g., less than 2 feet in length) are removed fromconsideration. Candidate gaps meeting these criteria are confirmed asreal gaps.

Thus, in one embodiment, gaps 304 are identified based on areasassociated with low to zero plant presence in aggregated plant presencedata 106 and their location is refined, as described in the nextsection, using information known about the field. In another embodiment,all potential gaps are identified based on areas associated with low tozero plant presence in aggregated plant presence data 106 andinformation known about the field is used to eliminate or rule-out falsegaps and confirm real gaps.

Field Characteristic Data

Field characteristic data 110 is information known about a field thatcan be used to heuristically identify gaps in a seed line or informationused to eliminate or rule-out locations on a seed line that may looklike a gap based on low plant presence values. In a field, growersinsert gaps between different genotypes of plants and it is common toplant an individual genotype in a single plot separated from adjacentplots by a gap. Though the length of the plots (or plot boundarymeasurement defined or provided by the grower) can vary, a common plotlength is about 20 feet and gaps between adjacent plots are commonlyaround 2 ft in length and this information can be used to positivelyidentify real gaps from areas of poor growth, for example, in the middleof a plot.

Accordingly, this information and other field characteristic data 110can be used to identify, refine, or discount the weight of candidategaps in the aggregated field data based on the location of a candidategap relative to an expected gap location. Other examples of fieldcharacteristic data 110 are a predetermined or field standard plot andgap lengths, GPS/location/position data for plot and field boundaries,seed line length, seed line orientation/direction, total number of seedlines and/or plots, stage of growth of plants in respective plots (sincesome plots may have been planted before others), type of plant in eachplot (since different plants have different growth rates and sizes), howmany plots are planted at a particular time (similar to stage ofgrowth), average plant separation, and other field parameters and plantcharacteristics. Field characteristic data 110 can also determinedheuristically using various statistical tools and/or methods. Forexample, Hough transform can used to determine the orientation of theseed lines, which is used to determine the orientation of field, amongother statistical methods.

In one embodiment, plot length data of field characteristic data 110corresponding to common plot/gap lengths (or the gap/plot lengthsidentified by the grower if different, for example) can be used toidentify, refine, or discount the locations of gaps. Based on the plotlength data, an expected location of the gaps in field 300 can bedetermined and subsequently projected onto aggregated plant presencedata 106. The plot length data, thus, can be used to identify theexpected locations where gaps should appear. For example, a candidategap relatively close to an expected gap (e.g., within a predeterminedthreshold length) can be identified as an actual gap. Conversely, acandidate gap located a length greater than the predetermined thresholdlength is likely a location associated with missing plants, not anactual gap between plots, and can be remove as a candidate gap.Additionally, the correct spacing between plots could also beauto-detected to identify gaps 304 from aggregated plant presence data106 based on the length of the gap relative to the average plantseparation in adjacent plots (or average separation for the field) andplant presence value.

Refinement

Refinement uses field characteristic data 110 to refine plot boundaries112 (e.g., start and end points) associated with each identified gap 304described above with respect to FIG. 6. Referring to FIG. 2, gapidentification module 236 further receives field characteristic data 110from system store 244 after performing gap determination 108, in oneembodiment. Since growers and machines can be a little bit off whenplanting seed lines 302, the plot boundary or seed line can beautomatically drawn by the gap identification module 236 for eachindividual plot to ensure an accurate mapping between field data 102 andthe actual plants in each plot. For example, the length between gaps 304as an average across all seed lines 302 can be used as a starting point.Then, the best gap in each individual seed line that is near a candidategap location can be automatically identified by the gap determinationmodule 236.

The best gap, in one embodiment, refers to an area of low plant presencevalues 104 with the greatest amount (or number) of characteristics orcriteria defined for an ideal or expected gap based on fieldcharacteristic data 110. These characteristics or criteria can includeat least the ideal or expected length of a gap, number of consecutiveplant presence values below a threshold value defined for gaps, numberof the same consecutive plant presence values below the threshold value,the location along a seed line corresponding to a multiple of thetheoretical plot length plus the theoretical gap length, and so forth.

FIG. 7 shows example process 700 for refining plot boundaries, in oneembodiment. In this example, the start and end points of each plot canbe refined using field characteristic data 110. Field characteristicdata 110 includes the length and/or location of field boundaries (604 a,604 b, collectively “604” corresponding to a line where seed lines 302begin and end), expected plot length, and expected gap length. Fieldcharacteristic data 110 may further include the number of planted seedlines 302. In one embodiment, data aggregation module 204 arranges plantpresence values 104 to reflect the corresponding shape of plots 302 infield 300 based on this known data. The gap identification module 236then initially segments field 300 using field boundaries 604 (shown inFIG. 6), the expected plot length, and the expected gap length toroughly identify theoretical gap boundaries (702 a, 702 b, collectively“702”) within each seed line 302.

Since the actual locations of gaps 304 may not perfectly correspond totheoretical gap boundaries 702 for each seed line 302 (e.g., based ondifferences in position data for plant presence values 104 in seed lines302, poor planting, or other effects), the location of gap boundaries702 can be refined based on plant presence value transitions, in oneembodiment. For example, gap identification module 236 can analyze theplant presence values for plant presence value gradients within athreshold distance of each boundary 702 and adjust placement of eachboundary 702 individually for each seed line 302. FIG. 7 showsdifferences between theoretical gap boundaries 702 and actual gapboundaries (704 a, 704 b, collectively “704”) in seed lines 302. In thisexample, theoretical gap boundaries 702 are substantially equal toactual gap boundaries 704 for gap 304 a in seed lines 302 a andtheoretical gap boundaries 702 are automatically adjusted to actual gapboundaries 704 for seed lines 302 b, 302 c, 302 d, and 302 e with arrowsindicating the direction of the adjustment. Thus, in variousembodiments, data across all (or at least a subset of) seed lines 302 isanalyzed to determine the locations of gaps 304 and then each individualseed line 302 is analyzed to refine the actual gap boundaries 704.

Occasionally, no plot boundaries (or gap 304) can be identified nearbyor within the threshold distance (e.g., as if the planter inadvertentlyplanted straight through a location where a gap should have beenlocated). In instances where no boundaries are identified, the expectedor theoretical location for where a gap should be located, as identifiedusing field characteristic data 110, can be assigned as a gap location.In one instance, the best gap closest to where a candidate gap should becan be used if there is no clear boundary, such as if there are weeds ina gap that increased the plant presence value for the gap and loweringany detectable plant presence value gradient.

User Assisted Method and Quality Assurance

At least portions of the above described process can be performedmanually by a user. For example, instead of automatically applying fieldcharacteristic data 110 to identify gaps 304, data aggregation module234 can generate a heat map 500 to allow a user to manually identifygaps 304 by providing an input to the field data analysis system 230.The fact that many seed lines are next to each other and the gaps shouldline up across all the seed lines can be a visual aid to a user inidentifying gaps 304, as can be seen from FIG. 6. In this example, itwould be apparent to a user looking at a plant presence value heat mapof FIG. 6 that false gaps 312 a, 312 b, and 312 c were not real gaps 304since all real gaps line up perpendicularly across field 300.Accordingly, a user can identify beginning and end points for each plotand, since the gaps are located at predetermined lengths, a user canquickly determine the likely location of each gap.

Additionally, manual input by the user may also be used as an addendumto the automatic processes in the previous sections. In one embodiment,data aggregation module 234 generates heat map 500 and plotvisualization module 240 presents heat map 500 to the user to receive aninput corresponding to the beginning and end points for each seed line302 (and perhaps the end of the seed line), and field data analysissystem 230 determines the location of plots 304 using the user inputsand field characteristic data 110. In this example, the seed lines 302of heat map are segmented into the one or more plots based on fieldcharacteristic data 110, such as plot length or/or gap length. Inanother embodiment, data aggregation module 204 arranges plant presencevalues 104 to reflect the corresponding shape of plots 302 for field 300and gap identification module 236 segments field 300 using fieldboundaries 604 (shown in FIG. 6), the expected plot length, and theexpected gap length to roughly identify theoretical gap boundaries 702,as described above. The heat map with identified theoretical gapboundaries 702 are presented to a user to adjust the location of gapboundaries 702 to actual gap boundaries 704. Thus, in variousembodiments, field data analysis system 230 analyzes data across seedlines 302 to determine the locations of gaps 304 and a user analyzeseach individual seed line 302 to manually refine the locations of theactual gap boundaries 704. In one embodiment, the user could select anarea of field 300 that includes plots and gaps, such as by drawing a boxaround at least a portion of field 300. Using this area selected orhighlighted by the user, gap identification module 236 can segment field300 using field boundaries 604 (shown in FIG. 6), the expected plotlength, and the expected gap length to roughly identify theoretical gapboundaries 702, as described above.

Data aggregation module 204 can, in one embodiment, arrange the plantpresence values 104 in the shape of plots for field 300 based on theposition data for the plant presence values and gap identificationmodule 236 can prompt a user to identify the gap locations for a singleseed line 302. Using the data for that seed line 302 provided by theuser, gap identification module 236 may compare the plant presencevalues on each side of a gap boundary between a plot and a gap anddetermines a plant presence value threshold for field 300 based on adifference between the compared plant presence values on each side ofthe boundary. Gap identification module 236 can, thus, analyze thecharacteristics of the actual gap boundaries for a single seed line 302that the user manually identified to subsequently identify other gapboundaries based similar characteristics or features among the otherseed lines 302.

Alternatively, the user identification of the gap locations for a singleseed line 302 can be used to refine the locations of the gaps in apreviously segmented heat map. For example, data aggregation module 204can arrange the plant presence values 104 in the shape of plots forfield 300 based on the position data for the plant presence values andgap identification module 236 can segment field 300 using any methoddescribed above (e.g., based on plant presence value gradients, theexpected location of the gaps based on the field characteristic data,and so forth), and prompt a user for input. In this embodiment, the userinput corresponds to the manual of the gap locations by the user toadjust the location of gap boundaries 702 to actual gap boundaries 704for a single seed line 302. Using the data for that seed line 302provided by the user, gap identification module 236 can adjust thelocation of gap boundaries 702 to the actual gap boundaries 704 for theother seed lines 302. Gap identification module 236 can, thus, analyzethe characteristics of the actual gap boundaries 704 for the seed line302 that the user manually adjusted to identify similar characteristicsor features among the other seed lines 302 to adjust their locationsaccordingly. Alternatively or additionally, gap identification module236 analyzes the characteristics of the actual gap boundaries 704 forthe seed line 302 that the user manually adjusted to compute and/orrefine thresholds used to identify the gaps instead of having a fixedset of thresholds (i.e., preset of the field or all fields). Thus, theusers refinements for one or more seed lines 302 are used to automatethe refinement of the actual gap boundaries 704.

In another example, once the gap identification module 236 hasidentified the gaps 108, and therefore the individual plots, the gaps304 and plots may be provided to a user for manual review. The manualreviewer may rate the identified gaps 108 and plots for accuracy,provide input to correct any errors they perceive, and/or flag a set ofdata including the identified gaps 108 and plots as having an error thatshould be escalated to a supervisor or reviewed by the farmer or othercustomer.

Seed Line Detection

In addition to identifying gaps in plots, the field data analysis system230 is also capable of identifying seed lines 302 in fields,particularly identifying which plants are located in which seed lines302. Identifying seed lines is of particular interest in the context ofusing UAVs to characterize the contents of a field, where at the outset,raw collected field data 102 may not be associated with particular seedlines, let alone plots.

The methods that may be used to identify seed lines are similar to thosethat may be used to identify gaps between plots. Rather than repeat thisdescription, the entirety of the contents of the sections titled “GapIdentification,” “Field Characteristic Data,” “Refinement,” and “ManualMethod and Quality Assurance” can be re-used to identify seed lines in afield, rather than gaps along a seed line.

Briefly, aggregate plant presence values 106 can be used to identifysemi-contiguous, often semi-linear strips of land within a field thatcontain at least a threshold amount of aggregate plant presence value,as well as interstitial strips of land having less than the thresholdamount of aggregate plant presence value. As above, generally seed lineswill be arranged approximately parallel to each other, of approximatelya same seed line length, and generally spaced approximately a uniformdistance apart from each other. Thus, similarly to the case for gaps,including false gaps and real gaps, the presence of extraneous plantssuch as weeds may mean that the aggregate presence values 106 includeboth false seed lines and real seed lines which the system 230 isconfigured to disambiguate.

More specifically, the same techniques for distinguishing false and realgaps can be used, including identifying false and real seed lines,identifying candidate seed lines which are then refined or eliminatedfrom consideration, identifying an initial layout of expected seed linesand then refining that initial version, and identifying the “best”possible seed line location when there is no clear indication as towhere the seed line should be.

Also as with the gap identification process, seed line identificationmay also use field characteristic data to identify, and refine seed linelocations. Examples of field characteristic data can again includeexpected seed line locations, expected lengths, and expected spacingsbetween seed lines, GPS/location/position data for plot and fieldboundaries, total number of seed lines, stage of growth of plants inrespective plots or seed lines (since some plots or seed lines may havebeen planted before others), type of plant in each plot or seed lines(since different plants have different growth rates and sizes), how manyplots or seed lines are planted at a particular time (similar to stageof growth), average plant separation, and other field parameters andplant characteristics.

Visualization/UI

Field data analysis system 202 further includes plant modeling module238, plot visualization module 240, and plot analysis module 242 thatprovide field data 102 and/or plant presence values 104 to allow a userto analyze various growth metrics of plants in field 300 in oneembodiment. Plant modeling module 238 generates a three-dimensionalvirtual model of an individual plant from field data 102. Moreinformation for generating plant virtual models can be found in U.S.Provisional Applications No. 62/163,147 and No. 62/279,599, which areincorporated by reference herein in their entirety.

Plot visualization module 240 uses the plant virtual models and/oraggregated plant presence data 106 of field 300 to generate a graphicaluser interface for user manual analysis. The virtual plant models ofmany plants in a plot allows in depth visual analysis, such as standcount, stand quality, rate of seedling emergence at a particular time,plant height, and so forth, in perspective with other plants in a plot.Additionally, plot visualization module 240 can render a plot imagelibrary containing all field data 102 for field 300 and plotreconstructions for every plot.

Further, data aggregation module 234 can generate a mapping between theheat map and field data 102 to allow a user to select a portion of theplot via the heat map and view, for example, the image captured or otherfield data 102 corresponding to the selected portion of the heat map.The heat map may include an indication of individual images, individualplants, and/or raw data (e.g., greenness, plant height, etc.) that, whenselected or “moused over,” cause the raw data/actual image correspondingto the selected image or plant to be displayed in an overlay, forexample, from the heat map.

Plot analysis module 242 performs (or enables a user to perform) variousanalytics on the data from each plot to generate tabular data andstatistics, such as plant count, plant spacing, building canopy heightdistributions, leaf area, and other key physiological parameters.

Additional Configuration Information

The foregoing description of the embodiments of the disclosure has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the disclosure to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of thedisclosure in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the disclosure may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a non-transitory, tangible computer readable storagemedium, or any type of media suitable for storing electronicinstructions, which may be coupled to a computer system bus.Furthermore, any computing systems referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

Embodiments of the disclosure may also relate to a product that isproduced by a computing process described herein. Such a product maycomprise information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the disclosure be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsis intended to be illustrative, but not limiting, of the scope of thedisclosure, which is set forth in the following claims.

1. A method comprising: receiving field data of a field, the fieldincluding a plurality of seed lines and one or more plots within theplurality of seed lines; determining a plant presence value for eachinstance of field data, each instance of field data corresponding to aportion of the field and each plant presence value numericallyrepresenting a measure of plant matter associated with each portion ofthe field; aggregating the plant presence values as a function ofposition within the field; identifying one or more gaps in theaggregated plant presence values based at least in part on plantpresence values below a plant presence value threshold; and segmentingthe aggregated plant presence values into the one or more plots based onthe one or more gaps.
 2. The method of claim 1, wherein identifying theone or more gaps in the aggregated plant presence values furthercomprises: determining an average row plant presence value for each rowof a plurality of rows perpendicular to the plurality of seed lines, theaverage row plant presence value including at least one plant presencevalue from two or more parallel seed lines of the plurality of seedlines; and identifying one or more rows with an average row plantpresence value below an average row plant presence value threshold asthe one or more gaps.
 3. The method of claim 1, wherein the field datais at least one of thermal image data, color image data, plant heightdata, or plant volume data received from at least one of a vehicle orunmanned aerial vehicle (UAV) capturing the field data.
 4. The method ofclaim 1, wherein the field data is image data captured using a camera,and wherein determining the plant presence value for each instance ofthe field data further comprises: determining an amount of green coloredpixels in each instance of the field data; and assigning the plantpresence value to each instance of field data based at least in part onthe amount of green colored pixels in each instance of field data. 5.The method of claim 1, wherein identifying the one or more gaps in theaggregated plant presence values further comprises: identifying plantpresence values below a plant presence value threshold as candidategaps; comparing characteristics of the candidate gaps to fieldcharacteristic data associated with the field to eliminate candidategaps with characteristics failing to match the field characteristic datawithin a threshold; and identifying remaining candidate gaps as the oneor more gaps between plots of the plurality of plots within the field.6. The method of claim 1, further comprising: obtaining position datafor each instance of field data, the aggregated plant presence valuesbeing arranged as the function of position within the field based on theobtained position data.
 7. The method of claim 6, wherein the aggregatedplant presence values are arranged into a plant presence heat maprepresenting varying measures of plant matter for the field, whereindark areas on the plant presence heat map represent portions of thefield with low plant matter density and light areas on the plantpresence heat map represent portions of the field with relatively highplant matter density.
 8. A method comprising: receiving a plurality ofinstances of field data, the field including a plurality of seed linesof a field and each instance of field data representing a portion of thefield; determining a plant presence value for each of the instances offield data, the plant presence value numerically representing a degreeof plant presence in the portion of the field associated with each ofthe instances field data; aggregating the plant presence values based onthe portions of the field represented by each of the plant presencevalues to generate a map representing plant presence values as afunction of position within the field; and identifying a plurality ofplots within the field based at least in part on plant presence valuesbelow a plant presence value threshold in the map.
 9. The method ofclaim 8, wherein identifying the plurality of plots within the fieldbased at least in part on plant presence values further comprises:identifying plant presence values below a plant presence value thresholdas candidate gaps; comparing characteristics of the candidate gaps tofield characteristic data associated with the field to eliminatecandidate gaps with characteristics failing to match the fieldcharacteristic data within a threshold; and identifying remainingcandidate gaps as actual gaps between plots of the plurality of plotswithin the field.
 10. The method of claim 9, wherein the fieldcharacteristic data is at least one of position data for plots in thefield provided by a grower, field boundary data, plot length, gaplength, or seed line length.
 11. The method of claim 8, whereinidentifying the plurality of plots within the field based at least inpart on plant presence values further comprises: determining an averagerow plant presence value for each row of a plurality of rowsperpendicular to the plurality of seed lines, the average row plantpresence value including at least one plant presence value from two ormore parallel seed lines of the plurality of seed lines; and identifyingrows with an average row plant presence value below an average row plantpresence value threshold as a gap between plots of the plurality ofplots within the field.
 12. The method of claim 8, further comprising:obtaining position data for each instance of field data, the aggregatedplant presence values being arranged as the function of position withinthe field based on the obtained position data.
 13. The method of claim8, wherein the field data corresponds to image data captured using acamera, and wherein determining the plant presence value for eachinstance of the field data further comprises: determining an amount ofgreen colored pixels in each instance of the field data; and assigningthe plant presence value to each instance of field data based at leastin part on the amount of green colored pixels in each instance of fielddata.
 14. The method of claim 13, wherein the amount of green pixels isat least one of a total number of green pixels, a percentage of greenpixels, or a ratio of green pixels to brown pixels in the field data.15. A non-transitory computer readable storage medium includinginstructions that, when executed by a processor, cause the processor to:receive field data of a field, the field including a plurality of seedlines and one or more plots within the plurality of seed lines;determine a plant presence value for each instance of field data, eachinstance of field data corresponding to a portion of the field and eachplant presence value numerically representing a measure of plant matterassociated with each portion of the field; aggregate the plant presencevalues as a function of position within the field; identify one or moregaps in the aggregated plant presence values based at least in part onplant presence values below a plant presence value threshold; andsegment the aggregated plant presence values into the one or more plotsbased on the one or more gaps.
 16. The non-transitory computer readablestorage medium of claim 15, wherein identifying the one or more gaps inthe aggregated plant presence values further comprises: determining anaverage row plant presence value for each row of a plurality of rowsperpendicular to the plurality of seed lines, the average row plantpresence value including at least one plant presence value from two ormore parallel seed lines of the plurality of seed lines; and identifyingone or more rows with an average row plant presence value below anaverage row plant presence value threshold as the one or more gaps. 17.The non-transitory computer readable storage medium of claim 15, whereinthe instructions that, when executed by the processor, further cause theprocessor to: obtain position data for each instance of field data, theaggregated plant presence values being arranged as the function ofposition within the field based on the obtained position data.
 18. Thenon-transitory computer readable storage medium of claim 15, whereinidentifying the one or more gaps in the aggregated plant presence valuesfurther comprises: identifying plant presence values below a plantpresence value threshold as candidate gaps; comparing characteristics ofthe candidate gaps to field characteristic data associated with thefield to eliminate candidate gaps with characteristics failing to matchthe field characteristic data within a threshold; and identifyingremaining candidate gaps as the one or more gaps between plots of theplurality of plots within the field.
 19. The non-transitory computerreadable storage medium of claim 18, wherein the field characteristicdata is at least one of position data for plots in the field provided bya grower, field boundary data, plot length, gap length, or seed linelength.
 20. The non-transitory computer readable storage medium of claim15, wherein the field data is at least one of thermal image data, colorimage data, plant height data, or plant volume data received from atleast one of a vehicle or unmanned aerial vehicle (UAV) capturing thefield data.